{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import argparse\n",
    "import sys\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import os.path\n",
    "import tarfile\n",
    "\n",
    "import numpy as np\n",
    "from six.moves import urllib\n",
    "import glob\n",
    "import scipy.misc\n",
    "import math\n",
    "\n",
    "mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)\n",
    "\n",
    "def get_inception_score(sample_test): \n",
    "    # Create the model\n",
    "    x = tf.placeholder(tf.float32, [None, 784])\n",
    "    W = tf.Variable(tf.zeros([784, 10]))\n",
    "    b = tf.Variable(tf.zeros([10]))\n",
    "    y = tf.matmul(x, W) + b\n",
    "    softmax = tf.nn.softmax(y)\n",
    "\n",
    "    # Define loss and optimizer\n",
    "    y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "    # The raw formulation of cross-entropy,\n",
    "    #\n",
    "    #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),\n",
    "    #                                 reduction_indices=[1]))\n",
    "    #\n",
    "    # can be numerically unstable.\n",
    "    #\n",
    "    # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw\n",
    "    # outputs of 'y', and then average across the batch.\n",
    "    cross_entropy = tf.reduce_mean(\n",
    "      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))\n",
    "    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
    "\n",
    "    sess = tf.InteractiveSession()\n",
    "    tf.initialize_all_variables().run()\n",
    "    # Train\n",
    "    for _ in range(1000):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
    "\n",
    "    # Test trained model\n",
    "    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    #print(sess.run(accuracy, feed_dict={x: mnist.test.images,\n",
    "    #                                  y_: mnist.test.labels}))\n",
    "\n",
    "\n",
    "    #Inception score\n",
    "    images = list(sample_test)\n",
    "    splits = 10\n",
    "    assert(type(images) == list)\n",
    "    assert(type(images[0]) == np.ndarray)\n",
    "    #assert(len(images[0].shape) == 3)\n",
    "    #assert(np.max(images[0]) > 10)\n",
    "    assert(np.min(images[0]) >= 0.0)\n",
    "    inps = []\n",
    "    for img in images:\n",
    "        img = img.astype(np.float32)\n",
    "        inps.append(np.expand_dims(img, 0))\n",
    "    bs = 100\n",
    "    preds = []\n",
    "    n_batches = int(math.ceil(float(len(inps)) / float(bs)))\n",
    "    for i in range(n_batches):\n",
    "        sys.stdout.write(\".\")\n",
    "        sys.stdout.flush()\n",
    "        inp = inps[(i * bs):min((i + 1) * bs, len(inps))]\n",
    "        inp = np.concatenate(inp, 0)\n",
    "        pred = sess.run(softmax, {x : inp})\n",
    "        preds.append(pred)\n",
    "    preds = np.concatenate(preds, 0)\n",
    "    scores = []\n",
    "    for i in range(splits):\n",
    "      part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]\n",
    "      kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))\n",
    "      kl = np.mean(np.sum(kl, 1))\n",
    "      scores.append(np.exp(kl))\n",
    "    #print(np.mean(scores), np.std(scores))\n",
    "    return np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From <ipython-input-2-8f124bb91a98>:103 in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "WARNING:tensorflow:From <ipython-input-1-b8085399cd3b>:48 in get_inception_score.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
      "image/png": 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Z4zgnRIAL3OS+4yPI2wET50FQcNvlhedNvQceLHIch0XDrL83LRedbLZBFrP99A7I3wPb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\nEH5IkrsQQvih/wdTOEvHZq/wsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125d3b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126670290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 1000; D loss: 0.6734; G_loss: 1.052; Inception_score: 1.132; penalty: 0.001048\n",
      "WARNING:tensorflow:From <ipython-input-1-b8085399cd3b>:48 in get_inception_score.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
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RfLU5m1+8vvJ4/fMHdqTKwI2nZnDjrGVNiuG3PxrEgM7JbDpwjNeW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\nSqkQpMldKaVCkCZ3pZQKQf8PI6fxJTuNCqoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1266155d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1265ce2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 2000; D loss: 0.7885; G_loss: 0.815; Inception_score: 1.727; penalty: 0.001116\n",
      "WARNING:tensorflow:From <ipython-input-1-b8085399cd3b>:48 in get_inception_score.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
      "image/png": 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syNvPT/6x8EjVU/pn8WXe/jAHHdjJ/Y/C54MJx3dlSI+O7DpUxr6iCkb0zqC0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\n+XKyxk8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129e93750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126126210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 3000; D loss: 0.7323; G_loss: 1.178; Inception_score: 3.356; penalty: 0.002122\n",
      "WARNING:tensorflow:From <ipython-input-1-b8085399cd3b>:48 in get_inception_score.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1263b2890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ed21490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 4000; D loss: 0.8039; G_loss: 1.173; Inception_score: 3.916; penalty: 0.001624\n",
      "WARNING:tensorflow:From <ipython-input-1-b8085399cd3b>:48 in get_inception_score.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
      "image/png": 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obdssnv6dWvD9+siM2vjfP5zMwC51mzPZuwOHHdwBGWk8ld6RVxbtY83DY2r+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\nq5RSXuCptodXJgQ/jO7kq4a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      "text/plain": [
       "<matplotlib.figure.Figure at 0x129efcbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SF5HYFNPlvre8il8u2sxv3t9Gbb1zbU4m37hwKP27dwo6mohIq4rJcj9wuJpf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\nFLZC9zYA0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124b4d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 5000; D loss: 0.8474; G_loss: 1.11; Inception_score: 4.178; penalty: 0.001978\n",
      "WARNING:tensorflow:From <ipython-input-1-b8085399cd3b>:48 in get_inception_score.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126a921d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1251c3810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 6000; D loss: 0.9273; G_loss: 1.075; Inception_score: 4.138; penalty: 0.00157\n",
      "WARNING:tensorflow:From <ipython-input-1-b8085399cd3b>:48 in get_inception_score.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
      "image/png": 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dRU5mKsYYXv56PfeedSSvz9/AtIWbeeua2r1iKquq6XG7dy3ZiSd2Z/Ks1dw0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\nubcHNvrsb7KPNbaMUkqpKIl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126500b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3cu+rm/nvd3Zyak4GT86czIh+GUHHEhE5aWKu3ItKj/CtJ9awaX8lfz81j3su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\n4mkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ad25f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 7000; D loss: 0.9577; G_loss: 1.002; Inception_score: 4.346; penalty: 0.002029\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import os\n",
    "import math\n",
    "\n",
    "mb_size = 128\n",
    "X_dim = 784\n",
    "z_dim = 100\n",
    "h_dim = 128\n",
    "\n",
    "lambd = 10\n",
    "\n",
    "mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)\n",
    "\n",
    "def get_perturbed_batch(minibatch):\n",
    "    return minibatch + 0.5 * minibatch.std() * np.random.random(minibatch.shape)\n",
    "\n",
    "def plot(samples):\n",
    "    fig = plt.figure(figsize=(4, 4))\n",
    "    gs = gridspec.GridSpec(4, 4)\n",
    "    gs.update(wspace=0.05, hspace=0.05)\n",
    "\n",
    "    for i, sample in enumerate(samples):\n",
    "        ax = plt.subplot(gs[i])\n",
    "        plt.axis('off')\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_aspect('equal')\n",
    "        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')\n",
    "\n",
    "    return fig\n",
    "\n",
    "def xavier_init(size):\n",
    "    in_dim = size[0]\n",
    "    xavier_stddev = 1. / tf.sqrt(in_dim / 2.)\n",
    "    return tf.random_normal(shape=size, stddev=xavier_stddev)\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, X_dim])\n",
    "X_p = tf.placeholder(tf.float32, shape=[None, X_dim])\n",
    "\n",
    "D_W1 = tf.Variable(xavier_init([X_dim, h_dim]))\n",
    "D_b1 = tf.Variable(tf.zeros(shape=[h_dim]))\n",
    "\n",
    "D_W2 = tf.Variable(xavier_init([h_dim, 1]))\n",
    "D_b2 = tf.Variable(tf.zeros(shape=[1]))\n",
    "\n",
    "theta_D = [D_W1, D_W2, D_b1, D_b2]\n",
    "\n",
    "z = tf.placeholder(tf.float32, shape=[None, z_dim])\n",
    "\n",
    "G_W1 = tf.Variable(xavier_init([z_dim, h_dim]))\n",
    "G_b1 = tf.Variable(tf.zeros(shape=[h_dim]))\n",
    "\n",
    "G_W2 = tf.Variable(xavier_init([h_dim, X_dim]))\n",
    "G_b2 = tf.Variable(tf.zeros(shape=[X_dim]))\n",
    "\n",
    "theta_G = [G_W1, G_W2, G_b1, G_b2]\n",
    "\n",
    "def sample_z(m, n):\n",
    "    return np.random.uniform(-1., 1., size=[m, n])\n",
    "\n",
    "def generator(z):\n",
    "    G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)\n",
    "    G_log_prob = tf.matmul(G_h1, G_W2) + G_b2\n",
    "    G_prob = tf.nn.sigmoid(G_log_prob)\n",
    "    return G_prob\n",
    "\n",
    "def discriminator(x):\n",
    "    D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)\n",
    "    out = tf.matmul(D_h1, D_W2) + D_b2\n",
    "    return out\n",
    "\n",
    "G_sample = generator(z)\n",
    "D_real = discriminator(X)\n",
    "D_fake = discriminator(G_sample)\n",
    "D_real_perturbed = discriminator(X_p)\n",
    "\n",
    "D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real, targets=tf.ones_like(D_real)))\n",
    "D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, targets=tf.zeros_like(D_fake)))\n",
    "disc_cost = D_loss_real + D_loss_fake \n",
    "gen_cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, targets=tf.ones_like(D_fake)))\n",
    "\n",
    "#Gradient penalty\n",
    "alpha = tf.random_uniform(\n",
    "    shape=[mb_size,1], \n",
    "    minval=0.,\n",
    "    maxval=1.\n",
    ")\n",
    "differences = X_p - X\n",
    "interpolates = X + (alpha*differences)\n",
    "gradients = tf.gradients(discriminator(interpolates), [interpolates])[0]\n",
    "slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), reduction_indices=[1]))\n",
    "gradient_penalty = tf.reduce_mean((slopes-1.)**2)\n",
    "disc_cost += lambd*gradient_penalty\n",
    "\n",
    "gen_train_op = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9).minimize(gen_cost, var_list=theta_G)\n",
    "disc_train_op = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9).minimize(disc_cost, var_list=theta_D)\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.initialize_all_variables())\n",
    "\n",
    "if not os.path.exists('out/'):\n",
    "    os.makedirs('out/')\n",
    "\n",
    "i = 0\n",
    "plotD = []\n",
    "plotG = []\n",
    "plot_inception = []\n",
    "\n",
    "for it in range(1, 200000):    \n",
    "    for _ in range(1):\n",
    "        X_mb, _ = mnist.train.next_batch(mb_size)\n",
    "        X_mb_p = get_perturbed_batch(X_mb)\n",
    "\n",
    "        _, D_loss_curr, penalty = sess.run(\n",
    "            [disc_train_op, disc_cost, gradient_penalty],\n",
    "            feed_dict={X: X_mb, X_p: X_mb_p, z: sample_z(mb_size, z_dim)}\n",
    "        )\n",
    "                  \n",
    "    _, G_loss_curr = sess.run(\n",
    "        [gen_train_op, gen_cost],\n",
    "        feed_dict={z: sample_z(mb_size, z_dim)}\n",
    "    )\n",
    "\n",
    "    plotD.append(D_loss_curr)\n",
    "    plotG.append(G_loss_curr)\n",
    "    \n",
    "    if it % 1000 == 0:\n",
    "        samples = sess.run(G_sample, feed_dict={z: sample_z(16, z_dim)})\n",
    "        \n",
    "        fig = plot(samples)\n",
    "        plt.savefig('out/{}.png'\n",
    "                    .format(str(i).zfill(3)), bbox_inches='tight')\n",
    "        i += 1\n",
    "        plt.close(fig)\n",
    "       \n",
    "        sample_test = sess.run(G_sample, feed_dict={z: sample_z(50000, z_dim)})\n",
    "        \n",
    "        inception_score = get_inception_score(list(sample_test))\n",
    "        plot_inception.append(inception_score)\n",
    "        \n",
    "        plotDn = np.array(plotD)\n",
    "        plt.plot(plotDn)\n",
    "        plotGn = np.array(plotG)\n",
    "        plt.plot(plotGn)\n",
    "        plt.show()\n",
    "        plot_In = np.array(plot_inception)\n",
    "        plt.plot(plot_In)\n",
    "        plt.show()\n",
    "        \n",
    "    if it % 1000 == 0:\n",
    "        print('Iter: {}; D loss: {:.4}; G_loss: {:.4}; Inception_score: {:.4}; penalty: {:.4}'\n",
    "              .format(it, D_loss_curr, G_loss_curr, inception_score, penalty))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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